Image 3_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.tif by Peng Zhu (277243)

“…Functional enrichment analyses revealed that high FLAD1 expression is involved in crucial biological processes like copper ion detoxification and heme complex assembly. …”

Table 2_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx by Peng Zhu (277243)

“…Functional enrichment analyses revealed that high FLAD1 expression is involved in crucial biological processes like copper ion detoxification and heme complex assembly. …”

Table 6_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx by Peng Zhu (277243)

“…Functional enrichment analyses revealed that high FLAD1 expression is involved in crucial biological processes like copper ion detoxification and heme complex assembly. …”

DataSheet1_The evolution of flexibility and function in the Fc domains of IgM, IgY, and IgE.pdf by Rosaleen A. Calvert (10039787)

“…</p>Results and discussion<p>We analysed the scattering curves in terms of contributions from a pool of variously bent models chosen by a non-negative linear least-squares algorithm and found that the four proteins form a series in which the proportion of acutely bent material increases: IgM-Fc < IgY-Fc < plIgE-Fc < huIgE-Fc. …”

Construction of the PRG score index using integrated machine learning algorithms. by Xiangxin Wu (22320058)

Subjects:

Line charts of the elbow method and the k-value selection algorithm: (A) elbow method; (B) k-value selection algorithm. by Jingnan Yan (20727331)

Subjects:

Sensitivity analysis of parameters <i>α</i> and <i>β</i>. by Haoran Cui (7235234)

“…And its output was smoother, the jitter amplitude was significantly reduced by 1/3, and the output torque was more stable. In addition, when the parameter <i>H</i>₀ changed to 2<i>H</i>₀, the overall speed curve did not change much, with only a slight overshoot. …”

YOLOv8n adds different loss function training process curve change plot. by Wenyuan Xu (2553028)

Subjects:

Strahler number (<i>H</i>), branching ratio () and length ratio () as functions of relative age (). by Jonathan E Kings (20818367)

<i>RMSE</i> comparison using all investigated methods in Table 3: MSX-60 module. by Ziad M. Ali (12516532)

Schematic showcasing main steps involved in the generation of a new daughter node <i>C</i>_<i>i</i> and subsequently a new branch <i>B</i>_<i>i</i>. by Diana C. de Oliveira (9220729)

Visualization for the module 1. by Zongjin Li (38031)

Images of partial benchmark functions. by ZeSheng Lin (20501356)

“…Secondly, the WOA’s position update formula was modified by incorporating inertia weight <i>ω</i> and enhancing convergence factor <i>α</i>, thus improving its capability for local search. …”

<i>M</i> = 100 realizations of time series <i>η</i> for stationary process, <i>N</i> = 5000 samples. by Martin Dodek (20623832)

Pseudo-code of MWOA algorithm. by Yi-Qiang Xia (20161326)

“…<div><p>This paper proposes the Modulated Whale Optimization Algorithm(MWOA), an innovative metaheuristic algorithm derived from the classic WOA and tailored for bionics-inspired optimization. …”

Parameters to configure the multi-staged vascularization using the PDCCO algorithm. by Eduardo G. Zilves (22209247)

Illustration of how (1 + 1)-WEA_v3 improves the target set when solving TSS. by Alexander Andreev (16822333)

Subjects:

<i>RMSE</i> comparison using all investigated methods in Table 1: RTC-F solar cell. by Ziad M. Ali (12516532)

<i>RMSE</i> comparison using all investigated methods in Table 2: PWP201 solar module. by Ziad M. Ali (12516532)

<i>M</i> = 100 realizations of time series <i>η</i> for non-stationary process, <i>N</i> = 40000 samples. by Martin Dodek (20623832)